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Structure Learning in Predictive Processing Needs Revision

Published version
Peer-reviewed

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Authors

de Wolff, E 
van Rooij, I 
Kwisthout, J 

Abstract

jats:titleAbstract</jats:title>jats:pThe predictive processing account aspires to explain all of cognition using a single, unifying principle. Among the major challenges is to explain how brains are able to infer the structure of their generative models. Recent attempts to further this goal build on existing ideas and techniques from engineering fields, like Bayesian statistics and machine learning. While apparently promising, these approaches make specious assumptions that effectively confuse structure learning with Bayesian parameter estimation in a fixed state space. We illustrate how this leads to a set of theoretical problems for the predictive processing account. These problems highlight a need for developing new formalisms specifically tailored to the theoretical aims of scientific explanation. We lay the groundwork for a possible way forward.</jats:p>

Description

Funder: donders institute


Funder: netherlands institute for advanced study in the humanities and social sciences; doi: https://doi.org/10.13039/501100001719

Keywords

5202 Biological Psychology, 5204 Cognitive and Computational Psychology, 52 Psychology

Journal Title

Computational Brain and Behavior

Conference Name

Journal ISSN

2522-0861
2522-087X

Volume Title

5

Publisher

Springer Science and Business Media LLC